Senior Engineer

Build and Evolve the GenAI Platform

• Develop reusable platform components for LLM access, orchestration, and agent-based workflows.
• Design and implement RAG and CAG patterns, including ingestion pipelines, retrieval strategies, and context assembly to ensure high-quality grounded outputs.
• Establish reusable prompting frameworks, templates, and standards to enable consistent and scalable use of GenAI across the organization.

Apply GenAI Fundamentals in Practice

• Demonstrate strong understanding of Generative AI core concepts, including:
  • Prompt engineering (structured prompting, system prompts, optimization)
  • Retrieval-Augmented Generation (RAG) and Context-Augmented Generation (CAG)
  • Programmatic prompting approaches (e.g., DSPy)
• Translate these concepts into robust, production-ready implementations, and reusable patterns for others.

Work Across End-User and Developer Ecosystems

• Leverage and integrate both:
  • End-user oriented tools such as Microsoft Copilot, Copilot Studio, and AI Builder
  • Developer-oriented frameworks and platforms such as Python, LangChain, Vertex AI, and Snowflake Cortex AI
• Optionally contribute to broader engineering stacks (e.g., Java, Spring AI, React) where needed.
• Actively use and promote coding copilots (e.g., GitHub Copilot, Gemini Code Assist, Claude Code) to accelerate development and improve engineering productivity.

Enablement and Consulting Mindset

• Act as a consultant within the organization, helping teams maximize the value of existing tools and platforms rather than defaulting to bespoke builds.
• Support engineers and business users in understanding how and where GenAI delivers value and guide them towards pragmatic solution patterns.
• Create reusable assets such as reference architectures, starter kits, and best practices.
• Communicate complex technical concepts in a clear, actionable way for both technical and non-technical audiences.

Quality, Governance, and Operational Excellence

• Implement evaluation, observability, and quality frameworks for LLM applications.
• Ensure solutions meet enterprise standards for reliability, security, and responsible AI adoption.
• Align with governance, risk, and compliance requirements in regulated environments.

Your skills and experience